In a conventional web-based information retrieval (IR) system, an end user who wants to view meta (i.e., description) information or the full contents of a stored data item sends a query to a backend system and then utilizes a browser to view the results of the query. As the number of stored data items increases and as the number of end users performing similar searches for stored data items increases, the risk of overloading IR system resources and rendering the IR system inoperable also increases. Known approaches for addressing the aforementioned risk and maintaining operability of IR systems include adding computing resources (e.g., adding computer memory, adding network bandwidth or adding central processing unit resources) or artificially limiting the size of the set of results (e.g., truncating a set of more than 500 results to a set that includes only the first 500 results). The additional computing resources approach is expensive to implement and may only be a temporary fix if, for example, the number of stored data items continues to increase. The artificial limitation approach prevents end users from finding desired results which are excluded from the artificially limited set of results. Such end users are inconvenienced either by never locating desired results or by performing the additional work of formulating a re-phrased query. Thus, there exists a need to overcome at least one of the preceding deficiencies and limitations of the related art.